urn:uuid:ead52325-8773-5d64-b1d1-6fe93a0660d2 New versions of this debate, revisions of its claims, Radar updates and research opportunities. 2026-10-17T00:00:00Z SocioLens urn:uuid:0b01dcd1-03b0-5361-88a8-73083f3c0889 2026-10-17T00:00:00Z

What are the early labour-market effects of generative AI?

urn:uuid:d3f7d835-fdba-5de5-91d0-f9667ff8f8e7 2026-10-14T00:00:00Z

What changed: The distributional ledger no longer says that an early-career earnings and hours null is reported. It now gives what the March 2026 revision of the paper reports for early-career workers (aged 22–25): their employment fell in several of the occupations, but their share of employment did not change differentially at workplaces that encourage chatbot use, with effects above about a third of a percentage point ruled out. The contradictory/supporting field now also notes the paper's workplace-level nulls for employment (total hours), job churn and the early-career share, and that Danmarks Nationalbank Working Paper 223 finds lower employment growth at AI-adopting firms. The public-facing claim, the 2% bound, the 11 occupations and the sample of about 25,000 workers in 7,000 workplaces are unchanged. Why: A recheck on 14 October 2026 read the full text of NBER Working Paper 33777 as revised in March 2026 (retitled "Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI"; previously "Large Language Models, Small Labor Market Effects"). Version 1.0 was built from the paper's abstract, and the HQ batch notes left "full Humlum/Vestergaard early-career ... details" open. The full text reports early-career results only as the employment share at adopting workplaces (Section 3.4, footnote 21), not as earnings or hours, and the same section reports no differential change in workplace employment (total hours) or job churn. The main result is confirmed in the full text: confidence intervals rule out average earnings and hours effects larger than 2% two years after ChatGPT, in 11 occupations, with 25,000 workers across 7,000 workplaces in the late-2024 survey round. The recheck and this revision were done by an AI agent reading the paper, not by a person.

urn:uuid:7d4b2153-7734-59ed-af34-9ee9360ae543 2026-10-14T00:00:00Z

What changed: The contradictory/supporting field no longer says that this result simply complements Humlum & Vestergaard. It now says the two Danish studies are in tension: the March 2026 revision of Humlum & Vestergaard finds no differential change in employment (total hours), job churn or the early-career share at workplaces that encourage chatbot use, through December 2024. The public-facing claim and the Nationalbank estimate are unchanged. Why: A recheck on 14 October 2026 read the full text of NBER Working Paper 33777 as revised in March 2026. Version 1.0 treated the two Danish studies as measuring different margins (incumbent earnings and hours against firm recruitment), but Section 3.4 of the revision also estimates workplace employment, job churn and the early-career share at adopting workplaces and finds no differential change. The studies differ in how adoption is measured (firm AI adoption against employer-encouraged chatbot use in 11 exposed occupations), in unit and in horizon (late 2025 against December 2024), so they need not contradict each other, but the difference is not explained by either paper as far as was read; the Nationalbank paper itself could not be opened for this recheck. The recheck and this revision were done by an AI agent, not by a person.

urn:uuid:e0875ce1-d26c-59ca-a728-da9d5d6efe5f 2026-10-14T00:00:00Z

The note on the nearest Danish work now says that its March 2026 revision (NBER w33777, retitled "Still Waters, Rapid Currents") no longer reports the 3–7% pass-through estimate the May 2025 version gave, and reports adopters' self-perceived earnings effects of 0 to 0.5% instead. Found when LD-02 was rechecked against the revised paper. The question, design, literature check and verdict are unchanged.

urn:uuid:f0075213-cf80-5c55-a61d-e94b68e67ae9 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:05f38936-8f50-5879-8b47-7ba3681e6f7b 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:ed496522-fc45-5c24-9aee-b786caf059d1 2026-10-13T00:00:00Z

Narrowed after the literature check. The Census CES-WP-26-56 paper already measures initial penalties for the most AI-exposed majors, reports that they attenuate further from entry and compares the loss with "a large recession", so early-career penalties and a rough recession comparison are no longer open for the US. Only press coverage (Chronicle of Higher Education) reports a two-year figure (the earnings gap narrowing from 13% to about 5%); the paper's abstract gives no horizon and that figure is unconfirmed in the paper (it may conflate the 5-point employment drop). The abstract compares the loss with "a large recession" without naming the Great Recession. Years three to five, occupational match and catch-up, a same-model benchmark against recession cohorts, and any non-US register evidence remain open, so the question should be narrowed to those.

urn:uuid:dc4601aa-407b-5f43-972a-b0d515555919 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:89920c00-b089-5012-a352-fe1e7374b974 2026-10-13T00:00:00Z

Literature check added: a second, deeper search for existing work, with its queries, new nearest work and limits. The question and verdict stand.

urn:uuid:0268b6e2-104d-5dbf-bbe6-036df06bd0f9 2026-10-13T00:00:00Z

Narrowed after the literature check. The text-as-data half of the design is already under way in France: CEET-Cnam has mapped French company agreements mentioning AI signed from 2017 (Connaissance de l'emploi no. 200, 2024, and a working paper covering 2017-2026), IRES (working paper 03.2025) has mapped AI clauses in French agreements, and an FES report (Kahancova et al., October 2026) analyses 108 European agreements with AI provisions. None of these, on the pages read, links agreement content to later hiring or training outcomes, and no study found does the event-study half. The opportunity should be narrowed to the outcome link, building on the existing French coding rather than proposing it as new.

urn:uuid:3f49422b-c371-57b3-be67-1d171eaa74a3 2026-10-12T00:00:00Z

When the same exposure measure, age bands, occupation coding and event-study specification are applied to US data and to Nordic population registers, how much of the gap between the large US young-worker divergence and the near-zero Nordic estimates is explained by occupation mix, education composition, sensitivity to interest-rate rises and cohort size, and how much remains?

urn:uuid:4661710f-178e-5b86-afe6-f0e740df5803 2026-10-12T00:00:00Z

If occupation-level AI exposure indices are replaced by AI-use rates measured in a nationally representative worker survey, does the relative fall in hiring of 22–25-year-olds into exposed occupations in US Current Population Survey data keep its size and sign, and how does the answer compare with exposure indices and chatbot-log usage measures?

urn:uuid:3f84b3fa-40d4-5301-94b3-079941e9a8fa 2026-10-12T00:00:00Z

For graduates who entered the labour market between 2021 and 2025, do the employment, earnings and occupational-match penalties of AI-exposed fields of study narrow, persist or widen over the first three to five years after graduation, and how do they compare with the well-documented scarring from graduating into a recession?

urn:uuid:871fca99-42db-5b96-b3d4-32ceeecb9892 2026-10-12T00:00:00Z

Among workers hired into junior positions before and after their employer began using AI, do promotion into higher-skill occupations, wage growth, recorded training and retention over the following two to four years differ between AI-adopting and comparable non-adopting firms?

urn:uuid:6c8d5fc5-cbd1-5f82-a8cf-a75fbb5c3e00 2026-10-12T00:00:00Z

When a firm starts using generative AI, how much does its value added per worker change over the following two to three years, and what share of that change shows up in the wages of incumbent workers who stay, compared with profits and with the wages of new hires?

urn:uuid:0ce23107-5d7b-54d7-b3fc-6603acac03e0 2026-10-12T00:00:00Z

In French firms, does signing a company-level collective agreement that addresses AI change the subsequent pattern of hiring (junior versus experienced, AI-related versus other roles) and training compared with similar firms in the same sector that have not signed one, and what do the agreements commit firms to?

urn:uuid:8a377541-9d02-5d35-92c3-63f5c06ae009 2026-09-25T00:00:00Z

What are the early labour-market effects of generative AI?